PONE-D-21-12960

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

PLOS ONE

Dear Dr. Greener,

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Yang Zhang

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Greener and Jones proposed to use automatic differentiation adapted from deep learning for learning all the parameters for molecular dynamics force field. They examined this method in a simple coarse-grained force field and used the learned force potential to study protein folding, scoring, and design on a set of small proteins. The method is useful and interesting, and the results seem promising. I have the following comments.

1. They mentioned that the modeling is trained on a set of 2004 diverse proteins up to 100 residues, but in the case studies (Table S1), the largest protein is only containing 35 amino acids. I wonder how their learned potential performs on larger proteins, e.g., ~100 amino acids, for folding, scoring, and design.

2. The test proteins are too few and small. They should include more and larger proteins for testing.

3. In the so-called protein design in this work, the authors did not systematically perform de novo sequence designs on fixed backbones. Instead, they evaluated the energy distributions of a set of sequence decoys with varying fraction of native sequence. Besides, they mutated residues according to PDB distributions. I wonder what is the performance of their learned force field for the computational de novo sequence design benchmark, i.e., “native sequence recapitulation rate”? Again, the authors just compared the native sequence and a set of sequence decoys on a set of mini proteins. This cannot represent a general performance of their useful learned potentials. I want to know 1) the performance of their potential on a larger set of larger proteins and 2) the comparison of their potential on native sequence recapitulation with other protein design approaches such as Rosetta and EvoEF2, just as they did for protein folding to compare with UNRES and CABS-fold.

4. Why NVE for training while NVT for testing? To my knowledge, NPT ensemble is often used for MD.

5. Some Figures are distractedly discussed in the manuscript, e.g., Figure 3B should be Figure 3C, and some figures/subfigures have never been mentioned at all.

Reviewer #2: The paper describes an interesting method for force-field optimization, which is based on machine learning. The Authors developed a new coarse-grained model of proteins, in which each residue is represented by 4 interaction sites (N, carbonyl-C, Calpha, and sidechain center). In optimization steps, whole microcanonical MD simulations are performed on the training proteins, starting from their experimental structures, and the potentials are optimized by using the Adam algoritm with automatic derivative calculations, the target function being log(1+rmsd), where rmsd is the root mean standard deviation from the experimental structure. Whole potential curves are determined; hence the potentials depend only on a single distance or angle/torsional angle, no dependence on orientation included. The optimized potentials were tested only against mini-proteins in de novo folding simulations but this does not seem to be a problem, because the Authors' objective was to demonstrate the principle rather than to produce a force field of practical application at this point. Moreover, the Authors have demonstrated that the potentials perform well in threading with minimization and in inverse folding.

The paper is very interesting and well written. I enjoyed readinig it. However, the following minor points should be addressed before it is accepted for publication:

1. Page 15, the "Training" section. Some more details should be given about the optimization procedure, in particular how te Adam optimizer with automatic derivatives works. Only the description of the calculation of the loss (target) function is provided but how are the gradients of the target function calculated? Referring to PyTorch is not a sufficient description.

2. How were the simulations with CABS-fold and UNRES-server carried out? The Authors state that both servers use secondary-structure prediction but, by default, both run in the ab initio mode. Therefore, the Authors should state that they input the secondary-structure information. Also, the UNRES server supports three force-field variants: the old FF2 (which is the default), OPT-WTFSA-2 [JCIM, 57, 2364-2377 (2017)] and the latest (and most advanced scale-consistent variant [NEWCT-9P (JCP, 150, 155104 (2019)]. I guess that the Authors used the FF2 variant, but this should be stated. Also, UNRES when run in MREMD mode produces 5 clusters of conformations. Did the Authors include the rank#1 structure or the lowest-RMSD structure in the analysis? Besides, UNRES server can also be run in canonical mode and it provides RMSD along the trajectory, from which the distribution can be extracted, which could be compared with those from the learned potential.

3. I am somehow puzzled that so long wall-clock time /10,000,000 steps is required (36 hrs on GPU; page 7, line 14 from the bottom). For 1,000,000 steps with the BBA mini-protein, UNRES server required 900 secs. wall-clock time, which translates to 2,5 wall-clock hrs per 10,000,000 steps on a single INTEL core (no GPU use). From my experience, CABS is comparable in timing or even faster (unfortunately, the CABS server was not functioning properly at the time I was writing this review). Model complicacy seems to comparable; CABS has 3 interaction sites (Calpha, Cbeta SC) and UNRES 2 (peptide groups and SC, but more complicated potentials) and, therefore, some optimization might be missing in force calculation. One thing that could be improved would be to store the numerical derivatives of the potentials in distance in addition to the potentials; this could save one subtraction and one division (point 3 in page 14). Also, symmetric divided differences could be used to improve the accuracy of the forces.

4. The description of the optimization procedure suggests that the experimental structures of the training proteins are only perturbed by running MD in the NVE mode. An immediate concern is that the potentials obtained that way will be biased towards the experimental structures. The fact that there are many training proteins probably makes this concern less serious but the Authors should provide more discussion about the transferability problem (both to non-native states and to other proteins). A maximum-likelihood approach, in which non-native structures are taken into account is described in refs. 37 and 38; also, there are recent papers by the D.E. Shaw group, in which they parameterize the all-atom force field to handle intrinsically-disordered proteins.

5. Figure 4. The superposition of the structure of villing headpiece obtained with the learned potentials does not seem to have the RMSD of 7.38 A. It rather looks like the perturbed native structure with about 2 A RMSD (see the left part of the panel that shows the RMSD distributions). Also, the fact that no conformation obtained in the simulation of villin started from the structure generated with secondary-structure prediction reached 12 low RMSD in 12 million steps raises concern. On the other hand, the simulation started from the experimental structure did not leave the native basin (the lef-bottom panel of Figure 4). If this simulation also lasted 12 million steps, the ergodicity of the simulations is of concern. Perhaps more shorter trajectories should be run.

6. For the reader's benefit, the Authors could mention other approaches at force-field optimization, including those of Crippenn and colleagues and Wolynes and colleagues of the 1990's, as well as the mutiscale coarse-grained force matching method developed by the Voth group.

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Reviewer #1: No

Reviewer #2: No

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PONE-D-21-12960R1

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

PLOS ONE

Dear Dr. Greener,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Yang Zhang

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The Authors have addressed all of my comments and I only have a minor suggestion: in page 17, lines 15-16 from the bottom the Authors state that they used Berendsen thermostat and the 0.02 Langevin scaling when running simulations on the UNRES server. The server uses either the Berendsen or the Langevin thermostat and if the Berendsen thermostat is specified, any Langevin thermostat settings are ignored, becuse the Langevin thermostat is not used. Therefore, unelss the Authors ran part of UNRES server simulations with the Berendsen and part with the Langevin thermostat (it should be stated in the manuscript, if so), they should delete the mention of the Langevin scaling factor.

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Reviewer #1: No

Reviewer #2: No

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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

PONE-D-21-12960R2

Dear Dr. Greener,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Yang Zhang

Academic Editor

PLOS ONE

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